Contextual situation analysis

ABSTRACT

A first context update indicative of a present state associated with an application is received. A first contextual situation, among a plurality of contextual situations associated with a plurality of conditions, that corresponds to the first context update is identified. Identifying the first contextual situation includes implementing a decision tree to minimize a number of the plurality of conditions to be evaluated, identifying the first contextual situation using the decision tree, and determining one or more actions to be performed corresponding with the first contextual situation.

RELATED APPLICATION

This application is a continuation application of U.S. patentapplication Ser. No. 16/607,021, filed Oct. 21, 2019, which is acontinuation of International Application No. PCT/US2018/025744, filedApr. 2, 2018, which claims the benefit of Provisional Application No.62/489,333, filed Apr. 24, 2017, the entire contents of all are herebyincorporated by reference herein.

TECHNICAL FIELD

This disclosure relates to the field of contextual situation analysis.

BACKGROUND

There are situations in which a processor is required to make decisionsbased on one or more received inputs. In an autonomous (or “driverless”vehicle), for example, a processor receives inputs (eg from camerasand/or radar) relating to the position and movement of other vehicles orpeople, and is required to make decisions such as whether to cause thevehicle to slow down, make an emergency stop, change direction etc.However, the most appropriate decision for the processor to make when itreceives a particular input (such as a particular observed behaviour ofanother road user) is likely to depend not only on that input but on thecircumstances (or context) of the situation in which the input occurs.For example, if a pedestrian steps into the road it may be appropriatefor the processor to instruct the vehicle to slow down or stop and/orchange direction. However, if the vehicle is already slowing to a stopat a red traffic light, and the pedestrian has stepped into the roadbeyond the red traffic light, the processor does not need to take anyfurther action. That is, the processor is required to evaluate both theinput and the context in which the input occurs in order to decide how,or whether, the vehicle should react. Although described here withspecific reference to an autonomous vehicle, contextual situationanalysis is not limited to the field of autonomous vehicles but occursin other fields.

SUMMARY

The following is a simplified summary of the disclosure in order toprovide a basic understanding of some aspects of the disclosure. Thissummary is not an extensive overview of the disclosure. It is intendedto neither identify key or critical elements of the disclosure, nordelineate a scope of the particular implementations of the disclosure ora scope of the claims. Its sole purpose is to present some concepts ofthe disclosure in a simplified form as a prelude to the more detaileddescription that is presented later.

In one implementation, a method includes receiving, by a compiler,source code including multiple rules. Each of the multiple of rulesincludes one or more of multiple conditions and is associated with oneof multiple contextual situations. The method also generates, using thesource code, a decision tree for the multiple rules. Generating thedecision tree includes minimizing a number of the multiple conditions tobe evaluated to determine whether a particular rule of the multiplerules has been satisfied. The method also generates a callable functionthat implements the decision tree. The callable function helps identifyat least one of the multiple contextual situations that corresponds to astate of an application interacted with by a user.

In another implementation, the method for generating the decision treefor the multiple rules includes determining a condition from themultiple conditions as a root node of the decision tree. The determinedcondition optimizes a number of the multiple conditions that areeliminated from subsequent evaluations.

In implementations, the method for generating the decision tree for themultiple rules includes organizing the decision tree with multiple nodesthat are associated with multiple conditions. The multiple nodes areorganized so that a condition of the multiple conditions having anoutcome that is implied by an outcome of an evaluation of a previousnode is not subsequently evaluated.

In implementations, the source code is a domain-specific language thatallows a satisfaction of a subset of conditions of the multipleconditions to be implied from a satisfaction of other conditions of themultiple conditions. In implementations, in the source code, conditionsfor a particular rule of the multiple rules are related by a logicalconjunction, and the each of the conditions includes a feature comparedto a constant using a comparison operator.

In some implementations, the method of determining the condition fromthe multiple conditions as the root node of the decision tree includesfor each condition of the plurality of conditions evaluating a conditionof the multiple conditions as true to determine a first number ofconditions where an outcome is implied in view of an evaluation of thecondition as true. The method also includes evaluating the condition ofthe multiple conditions as false to determine a second number ofconditions where the outcome is implied in view of an evaluation of thecondition as false. The method also includes determining a conditionfrom the multiple conditions to be the root node based on the firstnumber and the second number.

In implementations, a method receives a first context update for anapplication. The method determines whether one or more of multiple ruleshas been satisfied in view of the first context update. The multiplerules include a multiple conditions and are associated with multiplecontextual situations. The determining includes minimizing a number ofthe multiple conditions to be evaluated to determine whether aparticular rule of the multiple rules has been satisfied. The methodresponsive to determining a first rule of the multiple rules has beensatisfied, identifies a first contextual situation of the multiplecontextual situations that is associated with the first rule. The methodalso determines at least one action that is associated with the firstcontextual situation. In implementations, the method further causesperformance of a first action of the at least one actions determined forthe application. In an example relating to an autonomous vehicle,determining an action may for example comprise a processor of theautonomous vehicle determining that the vehicle needs to changedirection and/or brake—causing performance of a first action of the atleast one determined actions may comprise the processor instructing thebraking system of the vehicle to apply the brakes and/or instructing thesteering system of the vehicle to change the vehicle's direction oftravel. In another one simple example, the at least one action may be aplaylist of media items that is associated with the first contextualsituation, and the first action may be playback of a first media item ofthe playlist of media items to accompany the first contextual situationidentified for the application.

In implementations, the method includes receiving a second contextupdate for the application. The method also includes determining whetherone or more of the multiple rules has been satisfied in view of thesecond context update. The method responsive to determining a secondrule of the multiple rules has been satisfied in view of the secondcontext update, identifies a second contextual situation of the multiplecontextual situations that is associated with the second rule. Themethod includes determining at least one other action for the secondcontextual situation associated with the second rule.

In implementations, the method of determining whether one or more of themultiple rules has been satisfied in view of the first context updateincludes applying a data set associated with the first context update toa callable function that implements a decision tree. The parameters ofmultiple features identified by the data set are compared to conditionsof the multiple conditions that are associated with multiple nodes ofthe decision tree.

In implementations, the method of determining whether one or more of themultiple rules has been satisfied in view of the first context updateincludes keeping a record of conditions of the multiple conditions thathave outcomes that are explicitly and implicitly determined fromevaluated nodes of the multiple nodes of the decision tree.

In implementations, the method of determining whether one or more of themultiple rules has been satisfied in view of the first context updateincludes determining an initial condition of the multiple conditions toevaluate by determining a root node of the multiple nodes of thedecision tree. The root node indicates a feature and a conditionassociated with the feature. The method also includes evaluating theinitial condition identified by the root node in view of the data set ofthe first context update.

In implementations, the method of determining whether one or more of themultiple rules has been satisfied in view of the first context updateincludes responsive to the evaluation of the initial condition,evaluating a subsequent node of the decision tree where a number of themultiple conditions that have outcomes that are implied by theevaluation of the initial condition are eliminated from subsequentevaluation.

In implementations, each of the multiple rules includes one or moreconditions of the multiple conditions. The one or more conditions for aparticular rule of the multiple rules are related by a logicalconjunction, and each of the one or more conditions include a featurecompared to a constant using a comparison operator.

In implementations, the method may be performed by one or moreprocessing devices that control a device (such as an autonomous vehiclebased on the contextual situation of the device.

A further aspect of the present disclosure provides aprocessor-implemented method of controlling a device or processcomprising: generating a callable function according to anyimplementation described herein; identifying, using the callablefunction, a contextual situation; and controlling the device or processbased on the identified contextual situation. In an implementation,controlling the device or process based on the identified contextualsituation may comprise determining at least one action to be performedby the device or process based at least on the identified contextualsituation and optionally also based on one or more further inputs. Itmay further comprise the processor controlling or instructing the deviceor process to implement a first action of the at least one determinedactions. In additional implementations, one or more processing devicesfor performing the operations of the above described implementations aredisclosed. Additionally, in implementations of the disclosure, acomputer-readable storage medium (which may be a non-transitorycomputer-readable storage medium, although these implementations are notlimited to this) stores instructions for performing a method accordingto any of the described implementations. Also in other implementations,systems for performing a method according to any one of the describedimplementations are also disclosed. The system may comprise a memory;and a processing device, coupled to the memory, that is configured toperform a method according to any one of the described implementations.The memory may store computer-readable instructions that, when executedby the processing device, cause the processing device to perform amethod according to any one of the described implementations.

DESCRIPTION OF DRAWINGS

Various implementations of the present disclosure will be understoodmore fully from the detailed description given below and from theaccompanying drawings of various implementations of the disclosure.

FIG. 1 illustrates an example system architecture, in accordance withone implementation of the disclosure

FIG. 2 illustrates an example list of rules that can be used by acontextual music application, in accordance with an implementation ofthe disclosure.

FIG. 3 is an example system that uses contextual music application, inaccordance with implementations of the disclosure.

FIG. 4A illustrates generation and use of a decision tree by contextualmusic application, in accordance with implementations.

FIG. 4B illustrates a record used to keep track of whether a particularrule has been satisfied, in accordance with implementations.

FIG. 5 is a flow diagram illustrating a method for generating a callablefunction that implements a decision tree, in accordance with someimplementations.

FIG. 6 is a flow diagram illustrating a method for providing a playlistfor a current contextual situation associated with an applicationinteracted with by a use, in accordance with some implementations.

FIG. 7 is a block diagram illustrating an exemplary computer system,according to some implementations.

DETAILED DESCRIPTION

In an example of an autonomous vehicle interacting with a public roadenvironment the contextual situation or environment of the vehiclefrequently changes (e.g., from an “empty road” to “a congested road”).Contextual updates that include low-level information (e.g., “a personhas stepped into the road”) about the present contextual situation orenvironment may be monitored to determine if a particular high-levelcontextual situation (e.g., “need for emergency braking”) is takingplace (e.g., based on rules that map low-level information to specifichigh-level contextual situations). Contextual updates may occurfrequently (e.g., every 0.25 seconds) and include a large set ofinformation that is evaluated in view of a large set of rules. Theaforementioned presents many challenges, such as inability to providesufficient computational resources and power to frequently performcontextual situation analysis and identify one or more relevant actionsfor an identified contextual situation.

Aspects of the present disclosure address the above-mentioned and otherchallenges by generating a decision tree for evaluating multiple rulesused to identify particular contextual situations. The decision treeminimizes a number of conditions associated with the rules that are tobe evaluated to determine whether a particular rule has been satisfied,and identify a particular contextual situation that corresponds to acurrent state of an application or environment interacted with by theuser/device determined.

In some implementations, a context update is received by an application.The method determines whether one or more of the multiple rules havebeen satisfied in view of the context update, where the determiningminimizes a number of conditions to be evaluated to determine whether aparticular rule of the multiple rules has been satisfied. The methodincludes identifying a contextual situation based on determining that aparticular rule has been satisfied, and determining one or more actionsfor the contextual situation.

Accordingly, aspects of the present disclosure provides a mechanism thatminimizes computations involved in evaluating conditions to determinewhether a particular rule has been satisfied, determines from thesatisfied rule a contextual situation that corresponds to a state of theapplication or environment, and selects one or more actions for theidentified contextual situation. Aspects of the present disclosureresult in reduction of storage and computational (processing) resources,as well as associated resources such as battery power, because reducingthe number of conditions (and rules) for evaluation to determine aparticular contextual situation is more efficient than evaluating all ora majority of rules to determine a particular situational context.Furthermore, reducing the number of conditions (and rules) forevaluation to determine a particular contextual situation reduces thetime taken to determine an appropriate action to be taken, and this may,for example, improve the braking/stopping performance of an autonomousvehicle by reducing the time required for the processor of the vehicleto determine that the vehicle needs to stop to avoid a collision.

As noted, the present disclosure provides an improved method ofselecting one or more actions for an identified contextual situation,and may be applied in many fields. As another example, a user may beinteracting with an online gaming system where the contextual situationor environment of the game frequently changes (e.g., from a “peacefuldesert environment at night” to “a ride in a spaceship in outer space”).Contextual updates that include low-level information (e.g.,“altitude=1k meters”) about the present contextual situation orenvironment may be monitored to determine if a particular high-levelcontextual situation (e.g., “a ride in a spaceship in outer space”) istaking place (e.g., based on rules that map low-level information tospecific high-level contextual situations). When interacting with anapplication, users may prefer customized and varied music thatcorresponds to fast changing contextual situations. As in the case of anautonomous vehicle, contextual updates may occur frequently andsignificant computational resources and power may be required tofrequently perform contextual situation analysis and identify one ormore relevant actions for an identified contextual situation, such asdetermining a playlist for a contextual situation. For purposes ofclarity and simplicity, the term “playlist” or “media playlist” may be alist or an order or a grouping of different media items that can beviewed or displayed or played back in sequential or shuffled order withor without interaction from a user.

In the following, simple examples referring to selection of a playlistare used for illustration rather than limitation. As explained, however,it may be appreciated that features of the present disclosure may beapplied to multiple types of applications.

FIG. 1 illustrates an example system architecture 100, in accordancewith one implementation of the disclosure. The system architecture 100includes client devices 110A through 110Z, a network 105, a data store106, a content sharing platform 120, an application platform 143, acollaboration platform 145, and a contextual music platform 141.

In one implementation, network 105 may include a public network (e.g.,the Internet), a private network (e.g., a local area network (LAN) orwide area network (WAN)), a wired network (e.g., Ethernet network), awireless network (e.g., an 802.11 network or a Wi-Fi network), acellular network (e.g., a Long Term Evolution (LTE) network), routers,hubs, switches, server computers, and/or a combination thereof.

In one implementation, the data store 106 may be a memory (e.g., randomaccess memory), a cache, a drive (e.g., a hard drive), a flash drive, adatabase system, or another type of component or device capable ofstoring data. The data store 106 may also include multiple storagecomponents (e.g., multiple drives or multiple databases) that may alsospan multiple computing devices (e.g., multiple server computers). Inone implementation, data store 106 stores media items, such as videoitems.

The client devices 110A through 110Z may each include computing devicessuch as personal computers (PCs), laptops, mobile phones, smart phones,tablet computers, netbook computers, network-connected televisions, etc.In some implementations, client devices 110A through 110Z may also bereferred to as “user devices.” Each client device includes a mediaviewer 111. In one implementation, the media viewers 111 may beapplications that allow users to view or upload content, such as images,video items, web pages, documents, etc. For example, the media viewer111 may be a web browser that can access, retrieve, present, and/ornavigate content (e.g., web pages such as Hyper Text Markup Language(HTML) pages, digital media items, etc.) served by a web server. Themedia viewer 111 may render, display, and/or present the content (e.g.,a web page, a media viewer) to a user. The media viewer 111 may alsoinclude an embedded media player (e.g., a Flash® player or an HTML5player) that is embedded in a web page (e.g., a web page that mayprovide information about a product sold by an online merchant). Inanother example, the media viewer 111 may be a standalone application(e.g., a mobile application or app) that allows users to view digitalmedia items (e.g., digital video items, digital images, electronicbooks, etc.). According to aspects of the disclosure, the media viewer111 may be a content sharing platform application for users to record,edit, and/or upload content for sharing on the content sharing platform.As such, the media viewers 111 may be provided to the client devices110A through 110Z by the contextual music platform 141 and/or contentsharing platform 120. For example, the media viewers 111 may be embeddedmedia players that are embedded in web pages provided by the contentsharing platform 120. In another example, the media viewers 111 may beapplications that are downloaded from the contextual music platform 141.

In general, functions described in one implementation as being performedby the content sharing platform 120 can also be performed on the clientdevices 110A through 110Z in other implementations, if appropriate. Inother implementations, functions described as being performed by aparticular component may be performed by an alternative component. Inaddition, the functionality attributed to a particular component can beperformed by different or multiple components operating together. Thecontent sharing platform 120 can also be accessed as a service providedto other systems or devices through appropriate application programminginterfaces, and thus is not limited to use in websites.

In one implementation, the content sharing platform 120 may be one ormore computing devices (such as a rackmount server, a router computer, aserver computer, a personal computer, a mainframe computer, a laptopcomputer, a tablet computer, a desktop computer, etc.), data stores(e.g., hard disks, memories, databases), networks, software components,and/or hardware components that may be used to provide a user withaccess to media items and/or provide the media items to the user. Forexample, the content sharing platform 120 may allow a user to consume,upload, search for, approve of (“like”), disapprove of (“dislike”),and/or comment on media items. The content sharing platform 120 may alsoinclude a website (e.g., a webpage) or application back-end softwarethat may be used to provide a user with access to the media items.

In implementations of the disclosure, a “user” may be represented as asingle individual. However, other implementations of the disclosureencompass a “user” being an entity controlled by a set of users and/oran automated source. For example, a set of individual users federated asa community in a social network may be considered a “user”. In anotherexample, an automated consumer may be an automated ingestion pipeline,such as a topic channel, of the content sharing platform 120.

The content sharing platform 120 may include multiple channels (e.g.,channels A through Z). A channel can be data content available from acommon source or data content having a common topic, theme, orsubstance. The data content can be digital content chosen by a user,digital content made available by a user, digital content uploaded by auser, digital content chosen by a content provider, digital contentchosen by a broadcaster, etc. For example, a channel X can includevideos Y and Z. A channel can be associated with an owner, who is a userthat can perform actions on the channel. Different activities can beassociated with the channel based on the owner's actions, such as theowner making digital content available on the channel, the ownerselecting (e.g., liking) digital content associated with anotherchannel, the owner commenting on digital content associated with anotherchannel, etc. The activities associated with the channel can becollected into an activity feed for the channel. Users, other than theowner of the channel, can subscribe to one or more channels in whichthey are interested. The concept of “subscribing” may also be referredto as “liking”, “following”, “friending”, and so on.

Once a user subscribes to a channel, the user can be presented withinformation from the channel's activity feed. If a user subscribes tomultiple channels, the activity feed for each channel to which the useris subscribed can be combined into a syndicated activity feed.Information from the syndicated activity feed can be presented to theuser. Channels may have their own feeds. For example, when navigating toa home page of a channel on the content sharing platform, feed itemsproduced by that channel may be shown on the channel home page. Usersmay have a syndicated feed, which is a feed including at least a subsetof the content items from all of the channels to which the user issubscribed. Syndicated feeds may also include content items fromchannels that the user is not subscribed. For example, the contentsharing platform 120 or other social networks may insert recommendedcontent items into the user's syndicated feed, or may insert contentitems associated with a related connection of the user in the syndicatedfeed.

Each channel may include one or more media items 121. Examples of amedia item 121 can include, and are not limited to, digital video,digital movies, digital photos, digital music, audio content, melodies,website content, social media updates, electronic books (ebooks),electronic magazines, digital newspapers, digital audio books,electronic journals, web blogs, real simple syndication (RSS) feeds,electronic comic books, software applications, etc. In someimplementations, media item 121 is also referred to as content or acontent item.

A media item 121 may be consumed via the Internet and/or via a mobiledevice application. For brevity and simplicity, a video item is used asan example of a media item 121 throughout this document. As used herein,“media,” media item,” “online media item,” “digital media,” “digitalmedia item,” “content,” and “content item” can include an electronicfile that can be executed or loaded using software, firmware or hardwareconfigured to present the digital media item to an entity. In oneimplementation, the content sharing platform 120 may store the mediaitems 121 using the data store 106. In another implementation, thecontent sharing platform 120 may store video items and/or fingerprintsas electronic files in one or more formats using data store 106.

In one implementation, the media items are video items. A video item isa set of sequential video frames (e.g., image frames) representing ascene in motion. For example, a series of sequential video frames may becaptured continuously or later reconstructed to produce animation. Videoitems may be presented in various formats including, but not limited to,analog, digital, two-dimensional and three-dimensional video. Further,video items may include movies, video clips or a set of animated imagesto be displayed in sequence. In addition, a video item may be stored asa video file that includes a video component and an audio component. Thevideo component may refer to video data in a video coding format orimage coding format (e.g., H.264 (MPEG-4 AVC), H.264 MPEG-4 Part 2,Graphic Interchange Format (GIF), WebP, etc.). The audio component mayrefer to audio data in an audio coding format (e.g., advanced audiocoding (AAC), MP3, etc.). It may be noted GIF may be saved as an imagefile (e.g., .gif file) or saved as a series of images into an animatedGIF (e.g., GIF89a format). It may be noted that H.264 may be a videocoding format that is block-oriented motion-compensation-based videocompression standard for recording, compression, or distribution ofvideo content, for example. In some implementations, the video items maybe consumed primarily for the audio component rather than the videocomponent. In one implementation, the media items are audio-only items,such as music items.

In implementations, content sharing platform 120 may allow users tocreate, share, view or use playlists containing media items (e.g.,playlist A-Z, containing media items 121). A playlist refers to acollection of one or more media items that are configured to play oneafter another in a particular order without user interaction. Inimplementations, content sharing platform 120 may maintain the playliston behalf of a user. In implementations, the playlist feature of thecontent sharing platform 120 allows users to group their favorite mediaitems together in a single location for playback. In implementations,content sharing platform 120 may send a media item on a playlist toclient device 110 for playback or display. For example, the media viewer111 may be used to play the media items on a playlist in the order inwhich the media items are listed on the playlist. In another example, auser may transition between media items on a playlist. In still anotherexample, a user may wait for the next media item on the playlist to playor may select a particular media item in the playlist for playback. Inanother example, contextual music application 140 may select media itemsfrom a playlist for playback on client device 110.

In implementations, a playlist may be associated with a particular useror users, or made widely available to users of the content sharingplatform 120. In implementations, where content sharing platform 120associates one or more playlists with a specific user or group of users,content sharing platform 120 may associated the specific user with aplaylist using user account information (e.g., a user account identifiersuch as username and password). In other implementations, contentsharing platform 120 may associate a playlist with additionalinformation (also referred to as “metadata” or “playlist metadata”herein). For example, a playlist may be associated with a title (e.g.,Jakob's playlist), information related to the playlist (e.g., when theplaylist was created, user account information), additional informationregarding the media items of the playlist (e.g., artist information,genre, tempo, a hyperlink (link) to the media item, etc.), orinformation regarding the electronic messages used to populate theplaylist with the media items (e.g., part or all the electronicmessages, sentiment identifiers associated with the electronic messages,topics associated with the electronic messages, sender or receiverinformation related to the electronic messages (such as name of sender),etc.). In some implementations, a playlist may be created by contentsharing platform 120, contextual music platform 141, application 142, ora user of the aforementioned.

In some implementations, content sharing platform 120 may makerecommendations of media items to a user or group of users. Arecommendation may be an indicator (e.g., interface component,electronic message, recommendation feed, etc.) that provides a user withpersonalized suggestions of media items that may appeal to a user. Inimplementations, a recommendation may be made using data from a varietyof sources including a user's favorite media items, recently addedplaylist media items, recently watched media items, media item ratings,information from a cookie, user history, and other sources.

In implementations, content sharing platform 120, application platform143, contextual music platform 141, or collaboration platform 145 may beimplemented on one or more servers 130. In implementations, the server130 (e.g., 130A-C) may be one or more computing devices (e.g., arackmount server, a server computer, etc.). The server 130 may beincluded in the content sharing platform 120, be an independent systemor be part of another system/platform, such as application platform 143,contextual music platform 141, or collaboration platform 145.

In implementations, collaboration platform 145 may be executed on server130C and store source code 144. In some implementations, a user oradministrator may access collaboration platform 145 to upload orotherwise modify source code 144. In some implementations, collaborationplatform 145 may be a document sharing platform where users may create,edit, and share documents. In other implementations, the source code 144may be stored on client device 110A or provided by application platform143 or contextual music platform 141. In other implementations,collaboration platform 145 may in part or wholly be implemented onclient device 110 or other platforms.

In implementations, application platform 143 may provide an applicationthat is accessed by a user through client devices 110A-110Z. In someexamples, application platform 143 executes a gaming environment, suchas a 3D-gaming environment or virtual gaming environment, for access byclient devices 110A-110Z. In implementations, application platform 143may be implemented on server 130A. Application platform 143 may executeapplication 142B (also referred to as “application with contextualsituations” herein). The application 142B may be in a particular stateat a given time, where the particular state corresponds to a contextualsituation. In implementations, a contextual situation may refer to astate or particular context of for example, an application, a user, aphysical environment, virtual environment (e.g., virtual reality), oraugment environment (e.g., augmented reality). In implementations, thecontextual situation may further refer to the state or particularcontext of the application that is interacted with by the user at aparticular point in time. For example, application 142 may be a gamingapplication and a contextual situation may be a high-level descriptionof the gaming environment at a particular time. For instance, thecontextual situation “in a peaceful forest at night” may reflect thecurrent gaming environment of an application 142B, where the gamingenvironment is concurrently displayed on client device 110. Inimplementations, a user may interact with the application 142B usingclient device 110. In still other implementations, application 142B maybe in part or wholly implemented on client device 110 (or on otherplatforms), as illustrated by application 142A on client device 110A.

In implementations, contextual music platform 141 may be implemented onserver 130B. In implementations, contextual music platform 141 mayexecute contextual music application 140B. In implementations,contextual music application 140B may in part or wholly be implementedon client device 110 (or on other platforms), as illustrated bycontextual music application 140A on client device 110A. Contextualmusic application 140 may include contextual music compiler 112 orcontextual music selector 113. In other implementations, contextualmusic compiler 112 and contextual music selector 113 may be separateapplications implemented on the same or different devices.

Although implementations of the disclosure are discussed in terms ofcontent sharing platforms and promoting social network sharing of acontent item on the content sharing platform, implementations may alsobe generally applied to multiple types of social networks that provideconnections between users or multiple types of systems where contextualsituation analysis may be applied. Implementations of the disclosure arenot limited to content sharing platforms that provide channelsubscriptions to users.

In situations in which the systems discussed here collect personalinformation about users, or may make use of personal information, theusers may be provided with an opportunity to control whether the contentsharing platform 120 collects user information (e.g., information abouta user's social network, social actions or activities, profession, auser's preferences, or a user's current location), or to control whetherand/or how to receive content from the content server that may be morerelevant to the user. In addition, certain data may be treated in one ormore ways before it is stored or used, so that personally identifiableinformation is removed. For example, a user's identity may be treated sothat no personally identifiable information can be determined for theuser, or a user's geographic location may be generalized where locationinformation is obtained (such as to a city, ZIP code, or state level),so that a particular location of a user cannot be determined. Thus, theuser may have control over how information is collected about the userand used by the content sharing platform 120.

FIG. 2 illustrates an example list of rules that can be used by thecontextual music application, in accordance with an implementation ofthe disclosure. Source code 200 may be similar to source code 144 asdescribed with respect to FIG. 1 . It may be noted that components ofFIG. 1 may be used herein to help describe FIG. 2 .

The list 200 can be stored in a tabular format including columns, suchas columns labeled contextual situations 250, rules 251, and playlists252, and rows such as rows 260A-J. In some implementations, the list 200includes source code that defines rules using a programming language(e.g., object-oriented language, scripting language, markup language,etc.), It may be noted that the list 200 is provided for purposes ofillustration, rather than limitation, and may have one or more columns,rows, or types of information.

As noted above, contextual situations 250 may refer to a state orparticular context of, for example, an application, a user, a physicalenvironment, virtual environment (e.g., virtual reality), or augmentenvironment (e.g., augmented reality). For example, contextualsituations 250 may describe particular states of a gaming environment,such as “Sandy Desert at Night,” (e.g., row 260B), “Zombie Siege,”(e.g., row 260H), or “Hunting” (e.g., row 260J). In implementations,each of the contextual situations 250 is associated with a rule 251 anda playlist 252. For example, contextual situation 250 of row 260A, “In aPeaceful Forest at Night,” is associated with rule 251 of row 260A(e.g., “isNight>0 . . . difficulty==‘PEACEFUL’”) and playlists 252A.

In implementations, a particular rule, such as rule 251 of row 260A, mayhave one or more associated conditions, such as conditions 257. In someimplementations, the satisfaction of conditions, such as conditions 257of the associated rule 251 of row 260A, indicates the existence of theassociated contextual situation 250 (e.g., “In a Peaceful Forest atNight”). In some implementations, conditions, such as conditions 257,are related by logical conjunctions, such as a logical “AND.” Forexample, all the conditions 257 are to be satisfied at the same timebefore the existence of the contextual situation 250, “In a PeacefulForest at Night,” is determined.

In implementations, each contextual situation 250 of rows 260A-J isassociated with a corresponding playlist 252A-J. In implementations, theplaylists 252 may include one or more media items, such as video itemsor audio items. Each of the playlists 252 may include an indicator tolocate the playlist or associated media items. For example, playlist252A may contain a universal resource locator (URL) that points to theplaylist stored on content sharing platform 120. In another example,playlist 252A may point to a folder or file stored locally on the userdevice. In still other implementations, one or more playlists 252 mayinclude additional information such as start or end timestamps thatidentify portions of a media item on a playlist for playback. Forexample, the start or end timestamps may indicate playback of aparticular media item should begin at minute 1:00 and end at minute1:45.

In some implementations, each condition 257 includes a feature 253compared to a constant 254 by a comparison operator 256. Inimplementations, a feature 253 may describe an element of a contextualsituation 250. For example, “altitude,” “isNight,” “isSkyVisible,”“biome,” and “difficulty” may be features of one or more contextualsituations 250 of a gaming application.

In some implementations, a constant 254 may include a string or number,such as an integer. In implementations, the comparison operators 256 forconstants 254 that are numbers may include equality or inequality (e.g.,equal to, not equal to, less than, greater than, less than or equal to,greater than or equal to). In implementations, the comparison operators256 for constants 254 that are strings may include equality, inequality,match, or does not match a regular expression.

In some implementations, source code 200 may be a domain-specificlanguage that allows for a satisfaction of some conditions 257 to beimplied from a satisfaction of other conditions. For example, if thecondition “altitude>10,000 meters” is true, then it may be logicallyimplied that the condition “altitude>1,000 meters” is also true. Inanother example, if the condition “biome==‘ocean’” is true, then it maybe logically reasoned that “biome==‘forest’” is not true. In someimplementations, the domain-specific language may implement aconstrained set of comparison operators 256 and include conditions 257that are related by logical conjunctions, as described above.

In some implementations, the list 200 may be available to the user forcreation and modification. For example, a user may create a fileincluding the list 200 using collaboration platform 145. The user maydefine particular contextual situations 250, define the rules 251, orassociate particular playlists 252 with the contextual situations 250.In other implementations, the user may select the media items containedin a particular playlist 152. In other implementations, the list 200 maybe created or managed by an administrator or developer.

FIG. 3 is an example system utilizing the contextual music application,in accordance with implementations of the disclosure. Inimplementations, system 300 may be implemented using system architecture100 described with respect to FIG. 1 . Features of FIG. 3 may includecomponents described in FIGS. 1-2 , as well as additional components.

In implementations, the list 200, as described with respect to FIG. 2 ,includes source code that defines rules using a programming language.The source code 200 may be sent to contextual music compiler 112 ofcontextual music application 140, where contextual music compiler 112may translate source code 200 into executable code. As described withrespect to FIG. 2 , source code 200 may include multiple rules, whereeach of the rules includes one or more conditions. In implementations,each of the multiple rules may be associated with a contextualsituation.

In one implementation, the contextual music compiler 112 uses sourcecode to generate a decision tree. In implementations, the decision treeincludes a root node that is connected to other nodes (also referred toas “subsequent nodes” herein) by branches. In one implementation, eachof the nodes of the decision tree may be defined by a particularcondition, for example “biome=‘forest’”, or a particular feature, suchas “biome.” In implementations, the decision tree may be used tominimize the number of conditions to be evaluated to determine whether aparticular rule of the multitude of rules has been satisfied. It may benoted that minimizing the number of conditions to be evaluated todetermine whether a particular rule has been satisfied may be performedin other manners.

In implementations, a single root node may be used and subsequent nodesare evaluated dependent on the outcome of the root node or one or moreprevious nodes. Evaluation may refer to a determination process todetermine the outcome of a particular condition. For example, theoutcome of an evaluation of a condition of a node of a decision tree maybe “true” or “false.” Responsive to the outcome of an evaluation of anode, other conditions associated with other nodes, may be known or mootand not need to be further evaluated. In implementations, the outcome ofan evaluation of a condition of a node may imply that other conditionsare satisfied, imply that other conditions are not satisfied, or implythat some rules cannot be satisfied (and the conditions associated withthe rules are moot and need not be evaluated), for example. Inimplementations, the decision tree may be constructed so that some orall implied conditions are not evaluated at subsequent nodes. Additionalinformation regarding decision tree is described with respect to FIG. 4.

In implementations, contextual music compiler 112 generates a callablefunction 370 that implements the decision tree. Callable function 370may be used to identify at least one of the multiple contextualsituations, as defined in the source code 200, which corresponds to astate of the application 142 that is interacted with by the user. Inimplementations, the callable function 370 may be sent to contextualmusic selector 113 of contextual music application 140.

In an implementation, callable function 370 applies a context update 272as input. In an implementation, a context update 272 may include a dataset that provides parameters for various features of a contextualsituation being executed by application 142. For example, context update272 may include “altitude=10,000 meters, biome=‘desert; . . . ” Inimplementations, the parameters for features identified in the data setare compared to conditions that are associated with nodes of thedecision tree. In implementations, callable function 370, responsive toapplying the context update 272, determines whether one or more of themultiple rules have been satisfied. In implementations, the callablefunction 370 implements the decision tree to make the determination thata rule has been satisfied to minimize a number of the multipleconditions to be evaluated to determine whether a particular rule of themultiple rules has been satisfied.

In one implementation, callable function 370 in conjunction withcontextual music application 140 may identify a particular rule that hasbeen satisfied responsive to applying the context update 272 to thecallable function 370. The callable function 370 of contextual musicapplication 140 may identify a contextual situation that is associatedwith the satisfied rule, responsive to determining the particular rulehas been satisfied. For example, the callable function may output one ormore identifiers that indicate one or more corresponding contextualsituations.

In implementations, responsive to determining the particular rule hasbeen satisfied and determining the contextual situation associated withthe rule, contextual music selector 113 may determine the particularplaylist that is associated with the satisfied rule. It may be notedthat in implementations, responsive to a single context update 272, oneor more rules (or none) may be satisfied and one or more correspondingcontextual situations may be identified. Responsive to identifyingmultiple satisfied contextual situations, multiple playlists may beidentified, and media items from a particular playlist of the multipleplaylists may be selected for playback. Responsive to determining theparticular playlist, contextual music selector 113 may retrieve or causeplayback of media items from the particular playlist. In someimplementations, contextual music selector 113 may query content sharingplatform 120 using an application program interface (API) for access orplayback of the particular playlist A. In implementations, contextualmusic selector 113 may cause a concurrent playback of a media item ofthe playlist A with the occurrence of the contextual situationidentified for the application 142. For example, a video item (e.g.,music video) may be caused to play in a browser or application on theuser device while the user is engaged in a particular contextualsituation executing on a gaming application.

In implementations, a media item may be randomly chosen from theplaylist A. In other implementations, various algorithms may be used tochoose a media item from the playlist by determining which media itemthe user would have the most affinity towards in view of the particularcontextual situation. It may be noted that in other implementations,playlists associated with particular contextual situations may beretrieved by different or multiple platforms, or may be retrieved localto the user device.

In some implementations, a context update may be mapped to one or morecontextual situations and multiple media items, such as audio-onlyitems, that are contained in the one or more playlist. Inimplementations, determining a media item from the one or playlists forplayback may include one or more factors, such as matching scores,variety settings, playback history, or user feedback.

In implementations, each of the media items of the identifiedplaylist(s) may be assigned a matching score. A matching score may beindicative of a media item's determined matching or affinity with anidentified contextual situation(s). For example, a media item with amatching score of 90 has a higher affinity with a particular contextualsituation than another media item with a matching score of 60 for thesame contextual situation. In implementations, responsive to receiving acontext update 272, if a new media item has a matching score that ishigher than the current media item that is presently being played, thenew media item will interrupt playback of the current media item (e.g.,fade out) and replace (e.g., fade in) the current media item. In otherimplementations, if playback of a current media item has ended (e.g.,reached the end of the song or user has interrupted playback), the nextmedia item may be determined from the most recent context update in amanner described herein. In some implementations where the playback ofthe current media item has ended and a context update is stale (receivedbefore a threshold amount of time), contextual music selector 113 maynot begin playback of a new media item. In implementations, afterplayback of a particular media item begins, the other media items of theone or more playlists are blocked from interrupting playback.

In some implementations, a variety setting may be controlled by a user,developer, or administrator. In implementations, a variety setting mayrefer to an indication, such as a numerical setting, that indicates therelative importance of diversity (e.g., importance of playback ofdifferent media items rather than playback of media items with thehighest matching score). The variety setting may provide a statisticalprobability (e.g., relative likelihood) of selecting media items forplayback where the items have different matching scores, where mediaitems with lower matching scores have a lower probability of beingselected. For example, the variety setting may be 25% and threecandidate audio-only items may have matching scores of 85, 75, and 65. Aprobability (e.g., ratio) may be determined for each candidateaudio-only item so that each item has a non-zero probability of beingselected. For instance, the probability that one of the candidateaudio-only items is selected is 100%. The probability that eachcandidate audio-only item may be selected may be determined by choosinga first ratio that allows each subsequent ratio to be 25% of thepreceding ratio and have all the ratios add to 100%. For instance, theprobability for the matching score-85 audio-only item to play is 16/21,the probability for the matching score-75 audio-only item is 25% ofthat, e.g., 4/21, and the probability of the matching score-65audio-only item is 1/21, e.g., 25% of that of the matching score-75audio-only item (note that 16/21+4/21+1/21=21/21=1).

In some implementations, playback history may be taken into account toselect media items from playlists for playback. For example, a rule maybe implemented where the last N-number of last played songs are notrepeated. In some implementations, user feedback may be used to changethe probabilities of playback. For example, selection of like or dislikeinput elements by a user may be used to change the probability ofplayback (e.g., relative play probabilities as described above withrespect to variety settings) of a particular media item. In still otherimplementations, contextual music selector 113 determines the fractionof times each candidate media item has been played as well as thefraction of times each candidate media item should be played e.g.,(relative play probabilities as described above with respect to varietysettings), and picks the media item that minimizes the relative entropy(Kullback-Leibler divergence) between the observed and desiredtimes-played distributions.

In some implementations, application 142 may generate frequent contextupdates 272. For example, context updates 272 may occur every 0.25seconds and contain a data set with dozens to hundreds of parameters. Insome implementations, contextual music selector 113 may use an API tocommunicate and request context updates 272 from application 142. Insome implementations, content recognition techniques may be used togenerate some or all of the data set of context update 272. For example,screen shot analysis may be implemented that identifies text andelements or features displayed by application 142 on client device.

In implementations, after a media item is played back on a user deviceresponsive to determining the contextual situation of the application142, contextual music selector 113 may receive a new context update 272.If the contextual music selector 113 determines, in view of the newcontext update 272, that the contextual situation for the applicationhas not changed, the contextual music selector 113 may continue playbackof the media item or choose another media item from the associatedplaylist for playback. If the contextual music selector 113 determines,in view of the new context update 272, that a new rule of the multiplerules has been satisfied, contextual music selector 113 may identifyanother contextual situation associated with the newly identified rule.Contextual music selector 113 may retrieve another playlist from contentsharing platform 120 for the new contextual situation, and causeplayback of a media item from the other playlist.

In one implementation, contextual music compiler 112 of contextual musicapplication 140 may generate a decision tree for the multiple rules bydetermining a condition from the multiple conditions to be the root nodeof the decision tree. In implementations, a single decision tree with asingle root node is used. In other implementations, multiple decisiontrees may be used. In implementations, the root node optimizes a numberof the plurality of conditions that are eliminated from subsequentevaluations. As noted above, in implementations, the outcome of anevaluation of a condition of a node may imply that other conditions aresatisfied, imply that other conditions are not satisfied, or imply thatsome rules cannot be satisfied (and the conditions associated with therules are moot and need not be evaluated). In implementations, impliedconditions may not need to be re-evaluated.

In one implementation, in order to construct the decision tree,contextual music compiler 112 may check every condition (or feature)that best approximates a 50-50 split between conditions (or rules) thatare eliminated from subsequent evaluation and conditions that are noteliminated from subsequent evaluation responsive to the outcome of theevaluation of a condition of the root node.

For example, contextual music compiler 112 may evaluate the condition“biome=‘forest’.” Evaluating the condition “biome=‘forest’” as trueimplies the outcome of 40 percent of the conditions and eliminates 40percent of the conditions (as described in source code 200) fromsubsequent evaluation. Evaluating the condition “biome=‘forest’” asfalse implies the outcome of 50 percent of the conditions and eliminates50 percent of the conditions from subsequent evaluation. The worst casesplit for the condition “biome=‘forest’,” irrespective of the outcome ofthe evaluation (e.g., “true” or “false”), is 40-60 (e.g., percentageconditions eliminated from a subsequent evaluation compared topercentage of conditions not eliminated from subsequent evaluationresponsive to the outcome of the evaluation of “biome=‘forest’”). Eachcondition may be evaluated in a similar manner as described above todetermine the condition of the root node. If the 40-60 split for thecondition “biome=‘forest’” is determined to be closest or one of theclosest to the target split, such as a 50-50 split, “biome=‘forest’” maybe chosen as a root node.

In other implementations, the decision tree may be organized withsubsequent nodes connected to each other and the root node by branches.It may be noted that a decision tree may be evaluated in serial mannersuch that the outcome of an evaluation of a previous node determineswhich subsequent node(s) to evaluate and which subsequent node(s) not toevaluate. In implementations, the nodes of the decision tree may beorganized so that a condition of the multiple conditions where theoutcome is implied (e.g., by the outcome of an evaluation of a previousnode) is not evaluated at a connected subsequent node.

In some implementations, the nodes subsequent to the root node may bedetermined after the root node is determined, and in a similar manner asthe root node is determined. For example, for determining subsequentnodes for each branch of the decision tree, contextual music compiler112 may check every remaining condition (e.g., conditions not determinedas previous nodes) for the one condition that best approximates a 50-50split between conditions (or rules) that are eliminated from subsequentevaluation and conditions that are not eliminated from subsequentevaluation responsive to the outcome of the evaluation of a condition ofthe corresponding node.

In an implementation, the decision tree may be implemented in a callablefunction 370 that receives context updates 272 as inputs. The callablefunction 370 may determine whether one or more of the multiple rules hasbeen satisfied in view of the context update 272. In one implementation,the callable function 370 uses the decision tree to identify the initialcondition to evaluate. In implementations, the callable function 370 maydetermine the root node of the decision tree, and determine from theroot node the associated feature and condition from which to begin thecontextual situation analysis. The callable function 370 may evaluatethe initial condition identified by the root node in view of the dataset of the context update 272. In implementations, the outcome of theevaluation may be either “true” or “false.” The outcome of theevaluation may direct the callable function 370 to follow a specificbranch or specific branches and evaluate corresponding subsequentnode(s). In some implementations, responsive to the evaluation of theinitial condition of the root node (or other nodes of the decisiontree), the callable function 370 evaluates a subsequent node of thedecision tree where a number of the plurality of condition that haveoutcomes that are implied by the evaluation of the initial condition areeliminated from subsequent evaluation.

In some implementations, callable function 370 or contextual musicselector 113 keeps a record (e.g., table or scoreboard) of theconditions that have outcomes that are explicitly and implicitlydetermined from the evaluated nodes of the decision tree. Inimplementations, the record may held keep track of whether or not aparticular rule has been satisfied. Once a rule has been satisfied, theassociated contextual situation is identified and the associatedplaylist is retrieved.

FIG. 4A illustrates generation and use of a decision tree 400, inaccordance with implementations. FIG. 4B illustrates a record 450 usedto keep track of whether one or more rules has been satisfied, inaccordance with implementations. As noted above, a decision tree 400 maybe used minimize the number of conditions to be evaluated to determinewhether one or more rules of the multitude of rules has been satisfied.Components of the previous FIGS. 1-3 are used to help describe FIG.4A-B. It may be noted that the number of elements, such as conditions,rules, and nodes, is provide for purposes of illustration, rather thanlimitation. In other implementations, another number of elements may beused.

In an implementation, decision tree 400 shows root node 410, and varioussubsequent nodes 411-419. The nodes are connected by branches, such asbraches 420 and 421. For example, root node 410 is connected to node 411by branch 420 and is connected to node 415 by branch 421. Conditionscorresponding to each node are identified as C1 through C11. Theconditions C1-C11 are as follows: C1: y<=2, C2: y<=3, C3: y<=5, C4: y>6,C5: y>3, C6: y>7, C7: x>3, C8: x<=5, C9: x>5, C10: x<=1, C11: x>7.

As noted above, a rule may include on or more conditions. For example,decision tree 400 may represent a decision tree for six rules, Rules1-6. For example, the Rules 1 through 6 are represented as follows: Rule1: C1, y<=2; Rule 2: C4, y>6; Rule 3: C2 AND C9, y<=3 AND x>5; Rule 4:C5 AND C3 AND C7 AND C8, y>3 AND y<=5 AND x>3 AND x<=5; Rule 5: C6 andC11, y>7 AND x>7; Rule 6: C6 AND C10, y>7 AND x<=1. Record 450 of FIG.4B illustrates the Rules 1-6 as regions R1 through R6, respectively, ofrecord 450. Record 450 shows a checkerboard having an X-axis (horizontalaxis) and Y-axis (vertical axis), where each checker represents an X-Yvalue. For example, region R1 corresponds to rule 1 (e.g., Rule 1: C1,y<=2). If condition C1 (y<=2) is “true,” all the checkboxes locatedbelow y=2, are checked and are also “true.” It may be noted that atleast of some of values corresponding to Rule 3 (e.g., R3) are also“true” if Rule 1 is “true,” as illustrated by the overlap of regions R1and R3.

In one implementation, given conditions C1-C11, a compiler (for examplethe contextual music compiler 112) generates decision tree 400 whereevery node 410-419 has 3 subtrees, rather than 2 subtrees. In someimplementations, 1 or more subtrees may be used. It may be noted thatone or more subtrees may be empty and not shown connected to aparticular node.

In an implementations, the 3 subtrees may include a subtree to evaluateif the current node's check evaluates as “true” (The “(Y)es” branch)(e.g., subtree 430A), a subtree to evaluate if the current node's checkevaluates as “false” (The “(N)o” branch) (e.g., subtree 430B), and asubtree to always (A) evaluate after evaluating the (Y)es or (N)o branch(e.g., subtree 430C). Subtree 430C represents an “always” subtree, andis to be evaluated if the condition of node 412 is evaluated as “true”or “false.” Subtree 430C may also be referred to an independent subtree,where the evaluations of conditions of the independent subtree areindependent from the evaluation of one or more nodes, such as node 412(e.g., the outcome of the nodes of the independent subtree cannot beexplicitly or implicitly determined from the evaluation of other relatedconditions or nodes).

In decision tree 400, root node 410 includes condition C3. Inimplementations, the condition (e.g., C3) of root node 410 gets checkedfirst by contextual music selector 113. As illustrated, irrespective ofwhether C3 evaluates as “true” or “false,” 3 out of 6 rules areeliminated (e.g., 50/50 split) from further evaluation.

In implementations, responsive to a condition being evaluated as “true”or responsive to an evaluation of a condition that implies outcomes(e.g., “true” or “false”) of other conditions (e.g., a branch is enteredthat implies a particular condition(s)), the corresponding explicit andimplied conditions get marked as “satisfied” on the record 450 for eachrelated rule that contain the explicit and implied conditions. It may benoted that in implementations, contextual music selector 113 may performthe evaluation of conditions or rules using decision tree 400 and record450.

In one example, if the condition C3 of root node 410 is “true,” C2 ofnode 411 gets checked next. If C2 is “true,” C1 of node 412 gets checked(which decides if Rule 1 is “true”), and irrespective of the outcome ofC1 of node 412, C9 of node 419 gets checked afterwards (which decides ifRule 3 is true). In another example, in case the check C2 of node 411 is“false” (e.g., not y<=3), the outcome of the evaluation of node 411implies condition C5 (y>3) (as illustrated). It may be noted thatimplied conditions may be represented by the decision tree asillustrated by branch 423. Responsive to the determining the impliedcondition C5 is satisfied, C5 on record 450 gets marked as “satisfied”and is not re-checked. Continuing the example, responsive to C2 beingevaluated as “false,” C8 is checked, and if “true,” C7 is checked (whichdecides if rule 4 is “true”).

In another example, responsive to condition C3 of root node 410 beingevaluated as “false,” C6 of node 415 is checked to exclude Rule 5 andRule 6 in the case C6 does not hold. If C6 is “true,” the outcomeimplies C4 (and hence Rule 2). If C6 is “false,” then C4 is checked todecide Rule 2. Also, if C6 is “true,” contextual music selector 113checks if C10 holds (which decides Rule 6), and only if C10 is “false,”then C11 needs to be checked (since C10 and C11 are mutually exclusive),which decides Rule 5.

It may be noted that by implementing decision tree 400, only 4 conditionevaluations have been performed to determine for each of the 6 ruleswhether one or more rules are “true” or “false.” It may be noted that abrute force evaluation may check all the conditions for all the rulesindependently and perform the evaluation of 12 conditions.

FIG. 5 is a flow diagram illustrating method 500 for generating acallable function that implements a decision tree, for example adecision tree as in FIG. 4A, in accordance with some implementations.Method 500 may be performed by processing logic that includes hardware(e.g., circuitry, dedicated logic, programmable logic, microcode),software (e.g., instructions run on a processing device to performhardware simulation), or a combination thereof. In one implementation,contextual music application 140, and in particular, contextual musiccompiler 112, may perform some or all the operations described herein.

Method 500 begins at block 505 where processing logic receives sourcecode defining multiple rules. Each of the multiple of rules includes oneor more of multiple conditions and is associated with one of multiplecontextual situations. At block 510, processing logic generates, usingthe source code, a decision tree for the multiple rules. Generating thedecision tree includes minimizing a number of the multiple conditions tobe evaluated to determine whether a particular rule of the multiplerules has been satisfied. At block 515, processing logic generates acallable function that implements the decision tree. The callablefunction identifies one of the multiple contextual situations thatcorresponds to a state of an application interacted with by a user.

Once the callable function has been generated, a processing device mayuse the callable function in controlling a device (such as an autonomousvehicle) or process. The processing device that uses the callablefunction in controlling the device or process may have generated thecallable function, but this implementation does not require this. Theprocessing device may use the callable function to control the device orprocess by identifying, using the callable function, a contextualsituation; and controlling the device or process based on the identifiedcontextual situation. In an implementation, the processing device maydetermine at least one action to be performed by the device or processbased at least on the identified contextual situation and optionallyalso based on one or more further inputs. The processing device may befurther controlling or instructing the device or process to implement afirst action of the at least one determined actions.

FIG. 6 is a flow diagram illustrating a method for providing a playlistfor a current contextual situation associated with an applicationinteracted with by a user in accordance with some implementations.Method 600 may be performed by processing logic that includes hardware(e.g., circuitry, dedicated logic, programmable logic, microcode),software (e.g., instructions run on a processing device to performhardware simulation), or a combination thereof. In one implementation,contextual music application 140, in particular contextual musicselector 113, may perform some or all the operations described herein.

Method 600 begins at block 605 where processing logic receives a firstcontext update for an application. At block 610, processing logicdetermines whether one or more of multiple rules have been satisfied inview of the first context update. The multiple rules include multipleconditions and are associated with multiple contextual situations. Thedetermining includes minimizing a number of the multiple conditions tobe evaluated to determine whether a particular rule of the multiplerules has been satisfied. At block 615, processing logic responsive todetermining a first rule of the multiple rules has been satisfied,identifies a first contextual situation of the multiple contextualsituations that is associated with the first rule. At block 620,processing logic determines a playlist of media items that is associatedwith the first contextual situation. At block 625, processing logiccauses playback of a first media item of the playlist of media items toaccompany the first contextual situation identified for the application.

FIG. 7 is a block diagram illustrating an exemplary computer system 700,in accordance with implementations. The computer system 700 executes oneor more sets of instructions that cause the machine to perform one ormore of the methodologies discussed herein. Set of instructions,instructions, and the like may refer to instructions that, when executedcomputer system 700, cause computer system 700 to perform one or moreoperations of contextual music application 140. The machine may operatein the capacity of a server or a client device in client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine may be a personal computer (PC), atablet PC, a set-top box (STB), a personal digital assistant (PDA), amobile telephone, a web appliance, a server, a network router, switch orbridge, or a machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single machine is illustrated, the term“machine” shall also be taken to include a collection of machines thatindividually or jointly execute the sets of instructions to perform oneor more of the methodologies discussed herein.

The computer system 700 includes a processing device 702, a main memory704 (e.g., read-only memory (ROM), flash memory, dynamic random accessmemory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM),etc.), a static memory 706 (e.g., flash memory, static random accessmemory (SRAM), etc.), and a data storage device 716, which communicatewith each other via a bus 708.

The processing device 702 represents one or more general-purposeprocessing devices such as a microprocessor, central processing unit, orthe like. More particularly, the processing device 702 may be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processing device implementing other instructionsets or processing devices implementing a combination of instructionsets. The processing device 702 may also be one or more special-purposeprocessing devices such as an application specific integrated circuit(ASIC), a field programmable gate array (FPGA), a digital signalprocessor (DSP), network processor, or the like. The processing device702 is configured to execute instructions of the system architecture 100and the contextual music application 140 for performing the operationsdiscussed herein.

The computer system 700 may further include a network interface device722 that provides communication with other machines over a network 718,such as a local area network (LAN), an intranet, an extranet, or theInternet. The computer system 700 also may include a display device 710(e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), analphanumeric input device 712 (e.g., a keyboard), a cursor controldevice 714 (e.g., a mouse), and a signal generation device 720 (e.g., aspeaker).

The data storage device 716 may include a non-transitorycomputer-readable storage medium 724 on which is stored the sets ofinstructions of the system architecture 100 and contextual musicapplication 140 embodying one or more of the methodologies or functionsdescribed herein. The sets of instructions of the system architecture100 and contextual music application 140 may also reside, completely orat least partially, within the main memory 704 and/or within theprocessing device 702 during execution thereof by the computer system700, the main memory 704 and the processing device 702 also constitutingcomputer-readable storage media. The sets of instructions may further betransmitted or received over the network 718 via the network interfacedevice 722.

While the example of the computer-readable storage medium 724 is shownas a single medium, the term “computer-readable storage medium” caninclude a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe sets of instructions. The term “computer-readable storage medium”can include a medium that is capable of storing, encoding or carrying aset of instructions for execution by the machine and that cause themachine to perform one or more of the methodologies of the presentdisclosure. The term “computer-readable storage medium” can include, butnot be limited to, solid-state memories, optical media, and magneticmedia.

In the foregoing description, numerous details are set forth. It will beapparent, however, to one of ordinary skill in the art having thebenefit of this disclosure, that the present disclosure may be practicedwithout these specific details. In some instances, well-known structuresand devices are shown in block diagram form, rather than in detail, inorder to avoid obscuring the present disclosure.

Some portions of the detailed description have been presented in termsof algorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It may be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise, it is appreciated that throughout thedescription, discussions utilizing terms such as “receiving”,“generating”, “determining”, “organizing”, “evaluating”, “retrieving”,“applying”, “identifying”, “keeping”, or the like, refer to the actionsand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(e.g., electronic) quantities within the computer system memories orregisters into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may include a general purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but not limited to, a type of diskincluding a floppy disk, an optical disk, a compact disc read-onlymemory (CD-ROM), a magnetic-optical disk, a read-only memory (ROM), arandom access memory (RAM), an erasable programmable read-only memory(EPROM), an electrically erasable programmable read-only memory(EEPROM), a magnetic or optical card, or a type of media suitable forstoring electronic instructions.

The words “example” or “exemplary” are used herein to mean serving as anexample, instance, or illustration. An aspect or design described hereinas “example’ or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs. Rather, use ofthe words “example” or “exemplary” is intended to present concepts in aconcrete fashion. As used in this application, the term “or” is intendedto mean an inclusive “or” rather than an exclusive “or.” That is, unlessspecified otherwise, or clear from context, “X includes A or B” isintended to mean the natural inclusive permutations. That is, if Xincludes A; X includes B; or X includes both A and B, then “X includes Aor B” is satisfied under one or more of the foregoing instances. Inaddition, the articles “a” and “an” as used in this application and theappended claims may generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Moreover, use of the term “an implementation” or “oneimplementation” or “an implementation” or “one implementation”throughout is not intended to mean the same implementation orimplementation unless described as such. The terms “first,” “second,”“third,” “fourth,” etc. as used herein are meant as labels todistinguish among different elements and may not necessarily have anordinal meaning according to their numerical designation.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Other implementations will beapparent to those of skill in the art upon reading and understanding theabove description. The scope of the disclosure may, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A method comprising: receiving, by a processingdevice, a first context update indicative of a present state associatedwith an application; identifying a first contextual situation, among aplurality of contextual situations associated with a plurality ofconditions, that corresponds to the first context update, whereinidentifying the first contextual situation comprises: implementing adecision tree to minimize a number of the plurality of conditions to beevaluated, and identifying the first contextual situation using thedecision tree; and determining one or more actions to be performedcorresponding with the first contextual situation.
 2. The method ofclaim 1, wherein the application is part of an online gaming system. 3.The method of claim 1, wherein the application is part of an autonomousvehicle system.
 4. The method of claim 1, further comprising: causingplayback of a first media item of a playlist of media items to accompanythe first contextual situation identified for the application.
 5. Themethod of claim 1, further comprising: receiving a second context updateindicative of a new state associated with the application; identifying asecond contextual situation, among the plurality of contextualsituations associated with the plurality of conditions, that correspondsto the second context update; and determining one or more other actionsto be performed with the second contextual situation.
 6. The method ofclaim 5, further comprising: causing playback of a second media item ofa playlist of media items to accompany the second contextual situationidentified for the application.
 7. The method of claim 1, whereinidentifying the first contextual situation, among the plurality ofcontextual situations associated with the plurality of conditions, thatcorresponds to the first context update comprises: applying a data setassociated with the first context update to a callable function thatimplements the decision tree, wherein parameters of a plurality offeatures identified by the data set are compared to conditions of theplurality of conditions that are associated with a plurality of nodes ofthe decision tree.
 8. The method of claim 7, wherein identifying thefirst contextual situation, among the plurality of contextual situationsassociated with the plurality of conditions, that corresponds to thefirst context update further comprises: keeping a record of conditionsof the plurality of conditions that have outcomes that are explicitlyand implicitly determined from evaluated nodes of the plurality of nodesof the decision tree.
 9. The method of claim 7, wherein identifying thefirst contextual situation, among the plurality of contextual situationsassociated with the plurality of conditions, that corresponds to thefirst context update further comprises: determining an initial conditionof the plurality of conditions to evaluate by determining a root node ofthe plurality of nodes of the decision tree, wherein the root nodeindicates a feature and a condition associated with the feature; andevaluating the initial condition identified by the root node in view ofthe data set of the first context update.
 10. The method of claim 9,wherein identifying the first contextual situation, among the pluralityof contextual situations associated with the plurality of conditions,that corresponds to the first context update further comprises:responsive to the evaluation of the initial condition, evaluating asubsequent node of the decision tree where a number of the plurality ofconditions that have outcomes that are implied by the evaluation of theinitial condition are eliminated from subsequent evaluation.
 11. Asystem, comprising: a memory; and processing device, coupled to thememory, to perform operations comprising: receiving a first contextupdate indicative of a present state associated with an application;identifying a first contextual situation, among a plurality ofcontextual situations associated with a plurality of conditions, thatcorresponds to the first context update, wherein identifying the firstcontextual situation comprises: implementing a decision tree to minimizea number of the plurality of conditions to be evaluated, and identifyingthe first contextual situation using the decision tree; and determiningone or more actions to be performed corresponding with the firstcontextual situation.
 12. The system of claim 11, the operations furthercomprising: causing playback of a first media item of a playlist ofmedia items to accompany the first contextual situation identified forthe application.
 13. The system of claim 11, the operations furthercomprising: receiving a second context update indicative of a new stateassociated with the application; identifying a second contextualsituation, among the plurality of contextual situations associated withthe plurality of conditions, that corresponds to the second contextupdate; and determining one or more other actions to be performed withthe second contextual situation.
 14. The system of claim 13, theoperations further comprising: causing playback of a second media itemof a playlist of media items to accompany the second contextualsituation identified for the application.
 15. The system of claim 11,wherein identifying the first contextual situation, among the pluralityof contextual situations associated with the plurality of conditions,that corresponds to the first context update comprises: applying a dataset associated with the first context update to a callable function thatimplements the decision tree, wherein parameters of a plurality offeatures identified by the data set are compared to conditions of theplurality of conditions that are associated with a plurality of nodes ofthe decision tree.
 16. The system of claim 15, wherein identifying thefirst contextual situation, among the plurality of contextual situationsassociated with the plurality of conditions, that corresponds to thefirst context update further comprises: keeping a record of conditionsof the plurality of conditions that have outcomes that are explicitlyand implicitly determined from evaluated nodes of the plurality of nodesof the decision tree.
 17. The system of claim 15, wherein identifyingthe first contextual situation, among the plurality of contextualsituations associated with the plurality of conditions, that correspondsto the first context update further comprises: determining an initialcondition of the plurality of conditions to evaluate by determining aroot node of the plurality of nodes of the decision tree, wherein theroot node indicates a feature and a condition associated with thefeature; and evaluating the initial condition identified by the rootnode in view of the data set of the first context update.
 18. The systemof claim 17, wherein identifying the first contextual situation, amongthe plurality of contextual situations associated with the plurality ofconditions, that corresponds to the first context update furthercomprises: responsive to the evaluation of the initial condition,evaluating a subsequent node of the decision tree where a number of theplurality of conditions that have outcomes that are implied by theevaluation of the initial condition are eliminated from subsequentevaluation.
 19. A non-transitory computer-readable medium comprisinginstructions that, responsive to execution by a processing device, causethe processing device to perform operations comprising: receiving afirst context update indicative of a present state associated with anapplication; identifying a first contextual situation, among a pluralityof contextual situations associated with a plurality of conditions, thatcorresponds to the first context update, wherein identifying the firstcontextual situation comprises: implementing a decision tree to minimizea number of the plurality of conditions to be evaluated, and identifyingthe first contextual situation using the decision tree; and determiningone or more actions to be performed corresponding with the firstcontextual situation.
 20. The non-transitory computer-readable medium ofclaim 19, the operations further comprising: causing playback of a firstmedia item of a playlist of media items to accompany the firstcontextual situation identified for the application.